Semantics in Adaptive and Personalised Systems: Methods, Tools and Applications
暫譯: 自適應與個性化系統中的語義:方法、工具與應用

Lops, Pasquale, Musto, Cataldo, Narducci, Fedelucio

  • 出版商: Springer
  • 出版日期: 2019-09-30
  • 售價: $4,200
  • 貴賓價: 9.5$3,990
  • 語言: 英文
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3030056171
  • ISBN-13: 9783030056179
  • 海外代購書籍(需單獨結帳)

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商品描述

This textbook gives a complete overview of the techniques and the methods for semantics-aware content representation and shows how to apply such techniques in various use cases, such as recommender systems, user profiling and social media analysis.

Throughout the book, the authors provide an extensive analysis of the techniques currently proposed in the literature and cover all the available tools and libraries to implement and exploit such methodologies in real-world scenarios.

The book first introduces the problem of information overload and the reasons why content-based information need to be taken into account. Next, the basics of Natural Language Processing are provided, by describing operations such as tokenization, stopword removal, lemmatization, stemming, part-of-speech tagging, along with the main problems and issues. Finally, the book describes the different approaches for semantics-aware content representation: such approaches are split into 'exogenous' and 'endogenous' ones, depending on whether external knowledge sources as DBpedia or geometrical models and distributional semantics are used, respectively. To conclude, several successful use cases and an extensive list of available tools and resources to implement the approaches are shown.

 

The textbook definitely fills the gap between the extensive literature on content-based recommender systems, natural language processing, and the different types of semantics-aware representations.

 

商品描述(中文翻譯)

這本教科書全面概述了語義感知內容表示的技術和方法,並展示了如何在各種使用案例中應用這些技術,例如推薦系統、用戶檔案和社交媒體分析。

在整本書中,作者對目前文獻中提出的技術進行了廣泛的分析,並涵蓋了所有可用的工具和庫,以在現實場景中實施和利用這些方法論。

本書首先介紹了信息過載的問題以及為什麼需要考慮基於內容的信息。接下來,提供了自然語言處理的基本知識,描述了如標記化、停用詞移除、詞形還原、詞幹提取、詞性標註等操作,以及主要的問題和挑戰。最後,本書描述了語義感知內容表示的不同方法:這些方法根據是否使用外部知識來源(如 DBpedia)或幾何模型和分佈語義,分為「外生」和「內生」兩類。最後,展示了幾個成功的使用案例以及可用工具和資源的廣泛列表,以實施這些方法。

這本教科書確實填補了關於基於內容的推薦系統、自然語言處理和不同類型的語義感知表示之間的廣泛文獻之間的空白。

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